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Abstract
Accurate characterization of fleet and individual aircraft usage spectrums would allow component retirement times to be based on actual aircraft usage rather than on an assumed worst case usage spectrum used in a traditional time-based maintenance approach. A key enabling technology for such a Usage or Condition Based Maintenance (UBM/CBM) program is Regime Recognition (RR). The development and validation of such algorithms commonly employs flight loads survey data, which captures critical regimes and the corner points of the operational envelope. Such maneuver examples are flown precisely and don't necessarily capture how fleet aircraft are flown. The maneuver generation approach presented herein presents the foundational elements of a methodology to augment existing flight loads survey data with statistically independent maneuvers that address the desire to capture the variation that would be observed in the fleet as a result of different pilots, vehicle load-out, and environmental conditions via a simulation based autopilot. Such a capability allows developers to cost-effectively conduct comprehensive sensitivity analyses and verification of RR algorithms and the end-to-end process, while reserving high-cost flight test data for true blind validation.

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  Maneuver Generation for Regime Recognition Design and Validation

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